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Sprint projectOct 26, 2026New York

Relational Prediction of Consequence and Collusive Behavior in AI Agents

Brittany Huckabee

Submitted to AI Collusion Research Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.

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This study seeks to investigate what happens when potentially colluding agents are required to predict the consequences of their actions to the wider environment—asking whether such relational representations can alter subsequent behavior without changing assigned reward objectives. We tested this hypothesis in a multiagent hospital simulation, comparing agents at a no-prediction baseline with two conditions requiring progressively broader forecasts, including patient consequences. The three conditions produced markedly different patterns of behavior. However, analysis revealed three serious design problems introduced by the coding agents that helped build the pilot. The experiment therefore could not establish whether the wider representation of consequence might be associated with more or less harmful collusion.

The principal contribution at this stage is methodological: identifying requirements for an interpretable test of whether explicit representation of consequence to coupled systems can influence the behavior of agents pursuing assigned objectives.

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  2. 2Limited. Addresses a real problem but with a generic or well-trodden approach. Incremental at best.
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Cite this project

@misc{huckabee2026relational,
  title = {{Relational Prediction of Consequence and Collusive Behavior in AI Agents}},
  author = {Brittany Huckabee},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/relational-prediction-of-consequence-and-collusive-behavior-in-ai-agents-ntt0}},
  url = {https://apartresearch.com/sprints/projects/relational-prediction-of-consequence-and-collusive-behavior-in-ai-agents-ntt0}
}

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